Portfolio of Md Touhidul Islam

Problem:
Blind and low-vision (BLV) individuals face numerous challenges while navigating urban and indoor environments due to a lack of object-level awareness in computer vision models. Existing datasets like ImageNet or MS-COCO lack annotations for many objects crucial to safe and effective BLV navigation—such as overhanging branches, sidewalk pits, or bus stops—hindering the development of accessible navigation aids. 

Solution:
We created a new object taxonomy (see Table 1) tailored to BLV navigation by:

• Analyzing 21 publicly available videos of BLV individuals navigating varied environments.

• Identifying 80+ relevant objects by reviewing the videos and noting those that affected navigation through interaction or potential hazards (see Table 1).

• Conducting a focus group study with 6 participants (2 blind, two low-vision, two sighted experts) to revise the list, resulting in a curated set of 90 objects across 15 semantic categories (see Fig. 2).

• Labeling 31 video segments with the presence/absence of these objects.

• Benchmarking seven vision models (YOLOv7, Mask R-CNN, RAM, BLIP, etc.) on this dataset to assess how well existing models recognize these accessibility-critical objects (see Figs. 1 and 3).